Abstract: Sausage Diving

“No one should see how laws or sausages are made.” –misattributed to Otto von Bismarck

This summer I plan to dive into the ugly world of Congressional politics. Specifically, I will be looking at the whip data for the 1993 decision to join NAFTA.

For those of you new to sausage-making, here is how a whip count works:

A Congressional “whip” tries to make sure everyone in their party votes the way the party wants. In the lead-up to an important vote, the Congressional whip will conduct a “whip count,” where they poll their members on their likelihood of voting for the bill. (In this case, it was a scale of 1-5, where “1” was “definitely yes,” and “5” was “definitely no.”) Once they have this information, the party can determine how likely the bill is to succeed, and whether it will be necessary to appropriate political favors to win the vote.

For major issues like NAFTA, the party conducts multiple whip counts. (Sometimes several whip counts in one day, in the hours leading up to the final vote.)

While previous research has looked at Congressional behavior during the NAFTA vote, it has not taken into account the whip data, which was only recently made publicly available. Last summer I reviewed and logged the whip data, and this summer I plan to conduct a statistical analysis of the results, and I will ultimately challenge or confirm the existing literature on the topic.

My dependent variable will be the tendency of a Congressman to change his mind toward voting “yes” or “no” to NAFTA. There will be several independent variables, including the percentage of union members in a Congressman’s district, the influence of corporate lobbying, and the presence of Presidential “gifts”—where the president grants earmark spending in exchange for a vote. I will conduct a regression analysis using Stata software to look for correlations between the dependent and independent variables.